Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA

Fine-tuning LLMs to 1.58bit: extreme quantization made easy

Introducing AutoRound: Intel’s Advanced Quantization for LLMs and VLMs

Understanding and Implementing Qwen3 From Scratch

A Detailed Look at One of the Leading Open-Source LLMs

LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection

arXiv:2606. 04050v1 Announce Type: cross Abstract: Existing quantization methods are fundamentally limited by rigid, integer-based bit-widths (e.

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

arXiv:2606. 26650v1 Announce Type: cross Abstract: In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs.

CAT-Q: Cost-efficient and Accurate Ternary Quantization for LLMs

In this paper, we present CAT-Q, Cost-efficient and Accurate Ternary Quantization, for compressing and accelerating LLMs. Unlike existing state-of-the-art ternary quantization methods that rely on data-intensive and costly quantization-aware training to mitigate severe performance degradation, CAT-Q is a simple yet effective post-training quantization scheme that is readily applicable to LLMs with diverse architectures and model sizes.